Change-Point Detection in the Volatility of Conditional Heteroscedastic Autoregressive Nonlinear Models

Mohamed Salah Eddine Arrouch, Echarif Elharfaoui and Joseph Ngatchou-Wandji ()
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Mohamed Salah Eddine Arrouch: Department of Mathematics, Faculty of Sciences, Chouaïb Doukkali University, El Jadida 24000, Morocco
Echarif Elharfaoui: Department of Mathematics, Faculty of Sciences, Chouaïb Doukkali University, El Jadida 24000, Morocco
Joseph Ngatchou-Wandji: EHESP French School of Public Health, Université de Rennes, 35043 Rennes, France

Mathematics, 2023, vol. 11, issue 18, 1-31

Abstract: This paper studies single change-point detection in the volatility of a class of parametric conditional heteroscedastic autoregressive nonlinear (CHARN) models. The conditional least-squares (CLS) estimators of the parameters are defined and are proved to be consistent. A Kolmogorov–Smirnov type-test for change-point detection is constructed and its null distribution is provided. An estimator of the change-point location is defined. Its consistency and its limiting distribution are studied in detail. A simulation experiment is carried out to assess the performance of the results, which are compared to recent results and applied to two sets of real data.

Keywords: change-points; CHARN models; conditional least-squares; mixing; tests (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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